An Hybrid device authentication algorithm for edge-based IoT networks
Bibliographic record
Abstract
The Internet of Things (IoT) has emerged as the highly significant technology in today's world. IoT enables both users and devices to access services according to their needs from any location at any time. The data produced by these devices are vast and sensitive. Edge computing is crucial in IoT, offering services such as low latency, efficient data and network management, privacy and security and enhanced mobility. Solutions for privacy and security based on edge computing are essential for safeguarding the services and data generated by smart homes. Additionally, most IoT devices have limited storage and computing capabilities. Ensuring reliable device authentication is crucial in IoT, presenting challenges, such as resource constraints, heterogeneity, network dynamics and the deployment of IoT devices in remote areas. An edge-based IoT network is employed to meet the security needs of constrained devices. In this study, we introduce a novel edge-based architecture for smart homes and protect data and information by implementing a hybrid authentication algorithm. This hybrid device authentication algorithm is later integrated with the CoAP protocol, as the devices communicate using the CoAP protocol, and its detailed analysis is presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".